The present invention relates generally to nodule segmentation, and more particularly to segmentation of nodules and vessels in Computed Tomography (CT) studies.
Lung cancer is a leading cause of cancer related death in the United States. However, when lung cancer is diagnosed and treated at its earlier and potentially more curable stage, better prognosis and higher survival rate can be achieved.
CT imaging uses x-ray equipment to obtain image data from different angles around the human body and then uses computer processing of the information to produce cross-sectional images of the body tissues and organs. CT imaging, which can provide detailed information regarding internal anatomic structures non-invasively, has been broadly used for early lung cancer screening and diagnosis. Using high-resolution volumetric pulmonary CT images, lung nodules, even with very small size, can be detected and the nodules followed through time to determine whether there are any changes in the nodules. Nodules are, for example, physical masses that have the radiographic appearances of hazy opacities in a CT image.
Nodules are more clearly shown in high resolution computed tomographic (HRCT) images than plain radiographs. In addition, the appearance of nodules in HRCT images is a highly significant finding as they often indicate the presence of an active and potentially treatable condition such as bronchioalveolar carcinomas or invasive adenocarcinoma.
Because nodules are typically associated with active lung disease, the presence of nodules often leads to further diagnostic evaluation, including, for example, lung biopsy. Thus, a computer-based segmentation can be of assistance to medical experts for diagnosis and treatment of certain types of lung disease. Accordingly, there is a need for a system and method of computer-based segmentation or differentiation of the nodules and the tissues that can be used to accurately and consistently segment nodules for quick diagnosis.
While nodule volume measure and subsequent calculation of growth rate are important clinic indices for cancer diagnosis, a variety of computer-based methods have been developed for fast, accurate, and consistent nodule segmentation and volume measure. One of the most common difficulties for computer-based methods is to remove attached vessels from the nodule segmentation. FIGS. 1 and 2 show CT images and enlargements from the CT images of a section of the lung. As can be seen from the enlarged portions of FIGS. 1 and 2, vessels 102 and 204 have a similar intensities as nodules 106 and 208. Therefore, vessels, 102 and 204 may erroneously be included when attempting to segment or differentiate the nodules 106 and 208.